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Fig. 1 in Taxonomic groups with lower movement capacity may present higher beta diversity
Fig. 1. Location of the 16 fragments sampled in Minas Gerais, Brazil (LOCALITY, municipality): AIU, Aiuruoca; BOC, Bocaina de Minas; CAM, Camanducaia; CAX, Caxambu; DEL, Delfim Moreira; EXT, Extrema; GUA, GuaxupÉ; MAR, Maria da FÉ; MON, Monte Belo; MVE, Monte Verde; PAS, Passa Quatro; POÇ, Poços de Caldas; POU, Pouso Alegre; SGS, SÃo Gonçalo do SapucaÍ; SRJ, Santa Rita de Jacutinga and VIR, VirgÍnia.
Figura 1 in Lista preliminar de los lepidópteros diurnos (Lepidoptera: Papilionoidea) presentes en el bosque El Pelejo, San Martín, Perú
Figura 1. (A) Localización de la zona de estudio situada al noreste del departamento de San Martín (B), en el Perú (C). / (A) Location of the study area located northeast of the department of San Martín (B), in Peru (C).
Figura 3 in Lista preliminar de los lepidópteros diurnos (Lepidoptera: Papilionoidea) presentes en el bosque El Pelejo, San Martín, Perú
Figura 3. Algunas especies de lepidópteros identificados: A. Heraclides thoas cinyras. B. Historis oidus dious. C. Anteos menippe. D. Catonephele acontius. E. Detritivora cleonus. F.Morpho menelaus. G. Parides sp. H-I. Caligo idomeneus, vistas dorsal y ventral (fotos de R. Seminario). / Some identified lepidopteran species: A. Heraclides thoas cinyras. B. Historis oidus dious. C. Anteos menippe. D. Catonephele acontius. E. Detritivora cleonus. F. Morpho menelaus. G. Parides sp. H-I. Caligo idomeneus, dorsal and ventral view (photos by R. Seminario).
Figura 2 in Lista preliminar de los lepidópteros diurnos (Lepidoptera: Papilionoidea) presentes en el bosque El Pelejo, San Martín, Perú
Figura 2. Aspecto de la zona de muestreo, mostrando su vegetación característica (foto de R. Seminario). / Aspect of the sampling area, showing its characteristic vegetation (photo by R. Seminario).
Figura 4 in Lista preliminar de los lepidópteros diurnos (Lepidoptera: Papilionoidea) presentes en el bosque El Pelejo, San Martín, Perú
Figura 4. Ejemplares de Lepidoptera en busca de sales minerales en una zona lodosa. Un individuo de Heliconius wallacei (parte inferior izquierda), uno de Perrhybris pamela (parte superior izquierda) y cuatro de Itaballia demophile (parte superior derecha) (foto de R. Seminario). / Lepidoptera specimens in search of mineral salts in a muddy area. One individual of Heliconius wallacei (bottom left), one of Perrhybris pamela (top left) and four of Itaballia demophile (top right) (photo by R. Seminario).
Finalist in IBM + ETH EcoHackathon XPRIZE competition PRESENTATION
<p>The <strong>AI+Environment Hackathon</strong>, co-organized by the AI Center and GainForest, was a two-week competition (May 17th – June 2nd) aimed at leveraging machine learning to generate actionable environmental insights. Participants had the unique opportunity to contribute to the prestigious XPRIZE competition while addressing real-world challenges using environmental data gathered from the Amazon rainforest, provided by GainForest.</p> <p>Key highlights of the hackathon included:</p> <ul> <li><strong>Mentorship:</strong> Participants were guided by a team of ecologists and technical experts to develop innovative solutions.</li> <li><strong>Recognition:</strong> Outstanding submissions were featured in the XPRIZE finals in July, included in an academic paper, and showcased in a poster session held at the AI Center.</li> <li><strong>Skill Certification:</strong> All participants received a certificate recognizing their technical contributions and skills.</li> </ul> <p>Participants tackled real-world challenges using data-driven methods, combining environmental science with machine learning. The event encouraged collaboration and hands-on problem-solving, culminating in a closing ceremony and poster session on June 7th at the AI Center.</p> <p>With coding skills as a prerequisite, the hackathon not only fostered innovation but also created opportunities for participants to engage with leading experts, build meaningful solutions, and contribute to sustainability efforts in the Amazon rainforest. Those with advanced skills could also participate as mentors, coaching teams throughout the competition.</p>
GlobalHighCO: Global Daily Seamless 1 km Ground-Level CO Dataset over Land (2018–Present)
<p>GlobalHighCO is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level CO dataset over land <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.93 and a root-mean-square error (RMSE) of 0.21 mg m<sup>-3</sup> on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighTEMmin: Daily Seamless 1 km Minimum Air Temperature Dataset for China (2003–Present)
<p>ChinaHighTEM is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) <strong>minimum air temperature</strong> (TEMmin) dataset for China <strong>from 2003 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.98 and a root-mean-square error (RMSE) of 1.53 ℃ on a daily basis.</p> <p>If you use the ChinaHighTEMmin dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):</p> <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. <em>Scientific Data</em>, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
ChinaHighTEMavg: Daily Seamless 1 km Average Air Temperature Dataset for China (2003–Present)
<p>ChinaHighTEM is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) <strong>average air temperature</strong> (TEMavg) dataset for China <strong>from 2003 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.99 and a root-mean-square error (RMSE) of 1.18 ℃ on a daily basis.</p> <p>If you use the ChinaHighTEMavg dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):</p> <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. <em>Scientific Data</em>, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
GlobalHighO₃: Global Daily Seamless 10 km Ground-Level O₃ Dataset over Land (2000–Present)
<p>GlobalHighO<sub>3</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the first big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) global ground-level maximum daily 8-hour average (MDA8) O<sub>3</sub> dataset over land <strong>from 2000 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 6.25 ppb on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
IUCLID templates for PPP Risk Assessment - Template 7.2 - Template for presenting the results of the OECD 106 evaluators checklist
<p>This word file contains the template for <strong>presenting the results of the OECD 106 evaluators checklist</strong>. The template has been draft based on the Technical report on the outcome of the pesticides peer review meeting on the OECD 106 evaluators checklist (EFSA, 2017).<br> The filled-in template shall be pasted in the "<strong>Any other information on results incl. tables</strong> " field of IUCLID document “<strong>Adsorption and desorption in soil</strong>”<strong> </strong>endpoint study record (Section 7.1.3.1 of active substance dataset).</p>
Correlation of urban avian species diversity present in heterogenous habitat types of the Silk city, Odisha, Eastern India
<p>This is the complete metadata and the R code required to do the analysis of the paper regarding birds of Berhampur city.</p>
NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model"
<p>NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model", submitted to Journal of Advances in Modelling the Earth System.</p> <p>The data are produced from an ensemble of six experiments based on the GO8p0 configuration of NEMO v4.0.1 on a global 1/4° grid, as described in the paper. The ensemble is intended to test the z~ vertical coordinate, and includes a control with the default "z-star" fixed coordinate, and five experiments with the z-tilde vertical coordinate, using a selection of values for the two z-tilde timescale parameters. The data includes time series of global mean ocean and ice fields; large-scale transports; and fields from diapycnal mixing analysis.</p> <p>The first part of each filename refers to the experiment from the ensemble ("zstar", "ztilde_5_30", "ztilde_10_30", "ztilde_20_30", ztilde_20_60" and "ztilde_40_60"); the following five-character string identifies the respective suite on the Met Office Rose system and the MASS archive system; and the rest of the name specifies the type of data contained in the file.</p>
FIGURE 5 AVONET presents raw morphological data for 90,020 in AVONET: morphological, ecological and geographical data for all birds
FIGURE 5 AVONET presents raw morphological data for 90,020 individual birds at an average of 8.1–9.0 individuals per species (varying by taxonomy), providing a foundation for a new generation of studies investigating or accounting for intraspecific variance. This figure illustrates how variance is partitioned for a key morphological trait (beak length). Left-hand panels show that most variance is explained at higher taxonomic levels (orders, family and species), whereas intraspecific (individual) variation is contrastingly low, supporting the use of species averages in comparative studies. Curves are normal distributions based on SD; percentages (%) show proportion of variance at each level. Right-hand panels show beak length variance within families and within species (restricting to families with>5 species and species with>5 individuals measured; note different axis scales in upper and lower panel). Sequential ranks show a 'hockey-stick' distribution with examples of the most extreme outlier family (Scolopacidae) illustrated. Extreme within-species values for beak variance may reflect polymorphism or, in some cases, measurement error
Data generated by the model presented in the research article entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell"
<p>This repository provides all the data and scripts necessary to reproduce the line plots shown in the manuscript entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell".</p>
Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health
<p>Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of viral spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus), given that several closely-related viruses have been discovered and sarbecovirus-host interactions have gained attention since SARS-CoV-2 emergence. We assessed sampling biases and modeled current distributions of bats based on climate and landscape relationships and project future scenarios for host hotspots. The most important predictors of species distributions were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in locations greater than 2 °C hotter in a fossil-fueled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations.</p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
Data to reproduce the results presented in Sehgal et al. 2022. Water Resources Research, https://doi.org/10.1029/2021WR030624 ("Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer")
<p>This repository consists data to reproduce results as presented in: "Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer", Water Resorces Research. Kindly refer to the readme.text file to navigate through the dataset.</p> <p> </p> <p> </p>
GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles (presentation video)
<p>Video of the presentation for the publication M. Kamal, A. Barua, C. Vitale, C. Laoudias and G. Ellinas, "GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles," 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021, pp. 1-7, doi: 10.1109/VTC2021-Fall52928.2021.9625567.</p>
Language Instruction for Presenting in Classroom Situation or the Other Situation
<p>Prologue The YouTube materials mentioned below all intended be used in a variety of situations, and this format is only for teaching and learning. Therefore, do not reuse for commercial purposes; instead, go to YouTube's home page<sup>1</sup> and practice verbal presentation on a daily basis; the first to third234 videos portray a presentation on the verbal process given by an academic.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.